Abstract base classes complement duck-typing by
providing a way to define interfaces when other techniques like
hasattr() would be clumsy or subtly wrong (for example with
magic methods). ABCs introduce virtual
subclasses, which are classes that don’t inherit from a class but are
still recognized by isinstance() and issubclass(); see the
abc module documentation. Python comes with many built-in ABCs for
data structures (in the collections.abc module), numbers (in the
numbers module), streams (in the io module), import finders
and loaders (in the importlib.abc module). You can create your own
ABCs with the abc module.

argument

A value passed to a function (or method) when calling the
function. There are two kinds of argument:

keyword argument: an argument preceded by an identifier (e.g.
name=) in a function call or passed as a value in a dictionary
preceded by **. For example, 3 and 5 are both keyword
arguments in the following calls to complex():

complex(real=3,imag=5)complex(**{'real':3,'imag':5})

positional argument: an argument that is not a keyword argument.
Positional arguments can appear at the beginning of an argument list
and/or be passed as elements of an iterable preceded by *.
For example, 3 and 5 are both positional arguments in the
following calls:

complex(3,5)complex(*(3,5))

Arguments are assigned to the named local variables in a function body.
See the Calls section for the rules governing this assignment.
Syntactically, any expression can be used to represent an argument; the
evaluated value is assigned to the local variable.

An object that supports the Buffer Protocol, like bytes,
bytearray or memoryview. Bytes-like objects can
be used for various operations that expect binary data, such as
compression, saving to a binary file or sending over a socket.
Some operations need the binary data to be mutable, in which case
not all bytes-like objects can apply.

bytecode

Python source code is compiled into bytecode, the internal representation
of a Python program in the CPython interpreter. The bytecode is also
cached in .pyc and .pyo files so that executing the same file is
faster the second time (recompilation from source to bytecode can be
avoided). This “intermediate language” is said to run on a
virtual machine that executes the machine code corresponding to
each bytecode. Do note that bytecodes are not expected to work between
different Python virtual machines, nor to be stable between Python
releases.

A list of bytecode instructions can be found in the documentation for
the dis module.

class

A template for creating user-defined objects. Class definitions
normally contain method definitions which operate on instances of the
class.

coercion

The implicit conversion of an instance of one type to another during an
operation which involves two arguments of the same type. For example,
int(3.15) converts the floating point number to the integer 3, but
in 3+4.5, each argument is of a different type (one int, one float),
and both must be converted to the same type before they can be added or it
will raise a TypeError. Without coercion, all arguments of even
compatible types would have to be normalized to the same value by the
programmer, e.g., float(3)+4.5 rather than just 3+4.5.

complex number

An extension of the familiar real number system in which all numbers are
expressed as a sum of a real part and an imaginary part. Imaginary
numbers are real multiples of the imaginary unit (the square root of
-1), often written i in mathematics or j in
engineering. Python has built-in support for complex numbers, which are
written with this latter notation; the imaginary part is written with a
j suffix, e.g., 3+1j. To get access to complex equivalents of the
math module, use cmath. Use of complex numbers is a fairly
advanced mathematical feature. If you’re not aware of a need for them,
it’s almost certain you can safely ignore them.

The canonical implementation of the Python programming language, as
distributed on python.org. The term “CPython”
is used when necessary to distinguish this implementation from others
such as Jython or IronPython.

decorator

A function returning another function, usually applied as a function
transformation using the @wrapper syntax. Common examples for
decorators are classmethod() and staticmethod().

The decorator syntax is merely syntactic sugar, the following two
function definitions are semantically equivalent:

Any object which defines the methods __get__(), __set__(), or
__delete__(). When a class attribute is a descriptor, its special
binding behavior is triggered upon attribute lookup. Normally, using
a.b to get, set or delete an attribute looks up the object named b in
the class dictionary for a, but if b is a descriptor, the respective
descriptor method gets called. Understanding descriptors is a key to a
deep understanding of Python because they are the basis for many features
including functions, methods, properties, class methods, static methods,
and reference to super classes.

An associative array, where arbitrary keys are mapped to values. The
keys can be any object with __hash__() and __eq__() methods.
Called a hash in Perl.

docstring

A string literal which appears as the first expression in a class,
function or module. While ignored when the suite is executed, it is
recognized by the compiler and put into the __doc__ attribute
of the enclosing class, function or module. Since it is available via
introspection, it is the canonical place for documentation of the
object.

duck-typing

A programming style which does not look at an object’s type to determine
if it has the right interface; instead, the method or attribute is simply
called or used (“If it looks like a duck and quacks like a duck, it
must be a duck.”) By emphasizing interfaces rather than specific types,
well-designed code improves its flexibility by allowing polymorphic
substitution. Duck-typing avoids tests using type() or
isinstance(). (Note, however, that duck-typing can be complemented
with abstract base classes.) Instead, it
typically employs hasattr() tests or EAFP programming.

EAFP

Easier to ask for forgiveness than permission. This common Python coding
style assumes the existence of valid keys or attributes and catches
exceptions if the assumption proves false. This clean and fast style is
characterized by the presence of many try and except
statements. The technique contrasts with the LBYL style
common to many other languages such as C.

expression

A piece of syntax which can be evaluated to some value. In other words,
an expression is an accumulation of expression elements like literals,
names, attribute access, operators or function calls which all return a
value. In contrast to many other languages, not all language constructs
are expressions. There are also statements which cannot be used
as expressions, such as if. Assignments are also statements,
not expressions.

extension module

A module written in C or C++, using Python’s C API to interact with the
core and with user code.

file object

An object exposing a file-oriented API (with methods such as
read() or write()) to an underlying resource. Depending
on the way it was created, a file object can mediate access to a real
on-disk file or to another type of storage or communication device
(for example standard input/output, in-memory buffers, sockets, pipes,
etc.). File objects are also called file-like objects or
streams.

There are actually three categories of file objects: raw
binary files, buffered
binary files and text files.
Their interfaces are defined in the io module. The canonical
way to create a file object is by using the open() function.

Mathematical division that rounds down to nearest integer. The floor
division operator is //. For example, the expression 11//4
evaluates to 2 in contrast to the 2.75 returned by float true
division. Note that (-11)//4 is -3 because that is -2.75
rounded downward. See PEP 238.

An arbitrary metadata value associated with a function parameter or return
value. Its syntax is explained in section Function definitions. Annotations
may be accessed via the __annotations__ special attribute of a
function object.

Python itself does not assign any particular meaning to function
annotations. They are intended to be interpreted by third-party libraries
or tools. See PEP 3107, which describes some of their potential uses.

__future__

A pseudo-module which programmers can use to enable new language features
which are not compatible with the current interpreter.

By importing the __future__ module and evaluating its variables,
you can see when a new feature was first added to the language and when it
becomes the default:

The process of freeing memory when it is not used anymore. Python
performs garbage collection via reference counting and a cyclic garbage
collector that is able to detect and break reference cycles.

generator

A function which returns an iterator. It looks like a normal function
except that it contains yield statements for producing a series
of values usable in a for-loop or that can be retrieved one at a time with
the next() function. Each yield temporarily suspends
processing, remembering the location execution state (including local
variables and pending try-statements). When the generator resumes, it
picks-up where it left-off (in contrast to functions which start fresh on
every invocation).

generator expression

An expression that returns an iterator. It looks like a normal expression
followed by a for expression defining a loop variable, range,
and an optional if expression. The combined expression
generates values for an enclosing function:

>>> sum(i*iforiinrange(10))# sum of squares 0, 1, 4, ... 81285

generic function

A function composed of multiple functions implementing the same operation
for different types. Which implementation should be used during a call is
determined by the dispatch algorithm.

The mechanism used by the CPython interpreter to assure that
only one thread executes Python bytecode at a time.
This simplifies the CPython implementation by making the object model
(including critical built-in types such as dict) implicitly
safe against concurrent access. Locking the entire interpreter
makes it easier for the interpreter to be multi-threaded, at the
expense of much of the parallelism afforded by multi-processor
machines.

However, some extension modules, either standard or third-party,
are designed so as to release the GIL when doing computationally-intensive
tasks such as compression or hashing. Also, the GIL is always released
when doing I/O.

Past efforts to create a “free-threaded” interpreter (one which locks
shared data at a much finer granularity) have not been successful
because performance suffered in the common single-processor case. It
is believed that overcoming this performance issue would make the
implementation much more complicated and therefore costlier to maintain.

hashable

An object is hashable if it has a hash value which never changes during
its lifetime (it needs a __hash__() method), and can be compared to
other objects (it needs an __eq__() method). Hashable objects which
compare equal must have the same hash value.

Hashability makes an object usable as a dictionary key and a set member,
because these data structures use the hash value internally.

All of Python’s immutable built-in objects are hashable, while no mutable
containers (such as lists or dictionaries) are. Objects which are
instances of user-defined classes are hashable by default; they all
compare unequal (except with themselves), and their hash value is derived
from their id().

IDLE

An Integrated Development Environment for Python. IDLE is a basic editor
and interpreter environment which ships with the standard distribution of
Python.

immutable

An object with a fixed value. Immutable objects include numbers, strings and
tuples. Such an object cannot be altered. A new object has to
be created if a different value has to be stored. They play an important
role in places where a constant hash value is needed, for example as a key
in a dictionary.

import path

A list of locations (or path entries) that are
searched by the path based finder for modules to import. During
import, this list of locations usually comes from sys.path, but
for subpackages it may also come from the parent package’s __path__
attribute.

importing

The process by which Python code in one module is made available to
Python code in another module.

importer

An object that both finds and loads a module; both a
finder and loader object.

interactive

Python has an interactive interpreter which means you can enter
statements and expressions at the interpreter prompt, immediately
execute them and see their results. Just launch python with no
arguments (possibly by selecting it from your computer’s main
menu). It is a very powerful way to test out new ideas or inspect
modules and packages (remember help(x)).

interpreted

Python is an interpreted language, as opposed to a compiled one,
though the distinction can be blurry because of the presence of the
bytecode compiler. This means that source files can be run directly
without explicitly creating an executable which is then run.
Interpreted languages typically have a shorter development/debug cycle
than compiled ones, though their programs generally also run more
slowly. See also interactive.

iterable

An object capable of returning its members one at a time. Examples of
iterables include all sequence types (such as list, str,
and tuple) and some non-sequence types like dict,
file objects, and objects of any classes you define
with an __iter__() or __getitem__() method. Iterables can be
used in a for loop and in many other places where a sequence is
needed (zip(), map(), ...). When an iterable object is passed
as an argument to the built-in function iter(), it returns an
iterator for the object. This iterator is good for one pass over the set
of values. When using iterables, it is usually not necessary to call
iter() or deal with iterator objects yourself. The for
statement does that automatically for you, creating a temporary unnamed
variable to hold the iterator for the duration of the loop. See also
iterator, sequence, and generator.

iterator

An object representing a stream of data. Repeated calls to the iterator’s
__next__() method (or passing it to the built-in function
next()) return successive items in the stream. When no more data
are available a StopIteration exception is raised instead. At this
point, the iterator object is exhausted and any further calls to its
__next__() method just raise StopIteration again. Iterators
are required to have an __iter__() method that returns the iterator
object itself so every iterator is also iterable and may be used in most
places where other iterables are accepted. One notable exception is code
which attempts multiple iteration passes. A container object (such as a
list) produces a fresh new iterator each time you pass it to the
iter() function or use it in a for loop. Attempting this
with an iterator will just return the same exhausted iterator object used
in the previous iteration pass, making it appear like an empty container.

A key function or collation function is a callable that returns a value
used for sorting or ordering. For example, locale.strxfrm() is
used to produce a sort key that is aware of locale specific sort
conventions.

There are several ways to create a key function. For example. the
str.lower() method can serve as a key function for case insensitive
sorts. Alternatively, an ad-hoc key function can be built from a
lambda expression such as lambdar:(r[0],r[2]). Also,
the operator module provides three key function constructors:
attrgetter(), itemgetter(), and
methodcaller(). See the Sorting HOW TO for examples of how to create and use key functions.

An anonymous inline function consisting of a single expression
which is evaluated when the function is called. The syntax to create
a lambda function is lambda[arguments]:expression

LBYL

Look before you leap. This coding style explicitly tests for
pre-conditions before making calls or lookups. This style contrasts with
the EAFP approach and is characterized by the presence of many
if statements.

In a multi-threaded environment, the LBYL approach can risk introducing a
race condition between “the looking” and “the leaping”. For example, the
code, ifkeyinmapping:returnmapping[key] can fail if another
thread removes key from mapping after the test, but before the lookup.
This issue can be solved with locks or by using the EAFP approach.

list

A built-in Python sequence. Despite its name it is more akin
to an array in other languages than to a linked list since access to
elements are O(1).

list comprehension

A compact way to process all or part of the elements in a sequence and
return a list with the results. result=['{:#04x}'.format(x)forxinrange(256)ifx%2==0] generates a list of strings containing
even hex numbers (0x..) in the range from 0 to 255. The if
clause is optional. If omitted, all elements in range(256) are
processed.

The class of a class. Class definitions create a class name, a class
dictionary, and a list of base classes. The metaclass is responsible for
taking those three arguments and creating the class. Most object oriented
programming languages provide a default implementation. What makes Python
special is that it is possible to create custom metaclasses. Most users
never need this tool, but when the need arises, metaclasses can provide
powerful, elegant solutions. They have been used for logging attribute
access, adding thread-safety, tracking object creation, implementing
singletons, and many other tasks.

A function which is defined inside a class body. If called as an attribute
of an instance of that class, the method will get the instance object as
its first argument (which is usually called self).
See function and nested scope.

Mutable objects can change their value but keep their id(). See
also immutable.

named tuple

Any tuple-like class whose indexable elements are also accessible using
named attributes (for example, time.localtime() returns a
tuple-like object where the year is accessible either with an
index such as t[0] or with a named attribute like t.tm_year).

A named tuple can be a built-in type such as time.struct_time,
or it can be created with a regular class definition. A full featured
named tuple can also be created with the factory function
collections.namedtuple(). The latter approach automatically
provides extra features such as a self-documenting representation like
Employee(name='jones',title='programmer').

namespace

The place where a variable is stored. Namespaces are implemented as
dictionaries. There are the local, global and built-in namespaces as well
as nested namespaces in objects (in methods). Namespaces support
modularity by preventing naming conflicts. For instance, the functions
builtins.open and os.open() are distinguished by
their namespaces. Namespaces also aid readability and maintainability by
making it clear which module implements a function. For instance, writing
random.seed() or itertools.islice() makes it clear that those
functions are implemented by the random and itertools
modules, respectively.

namespace package

A PEP 420package which serves only as a container for
subpackages. Namespace packages may have no physical representation,
and specifically are not like a regular package because they
have no __init__.py file.

The ability to refer to a variable in an enclosing definition. For
instance, a function defined inside another function can refer to
variables in the outer function. Note that nested scopes by default work
only for reference and not for assignment. Local variables both read and
write in the innermost scope. Likewise, global variables read and write
to the global namespace. The nonlocal allows writing to outer
scopes.

new-style class

Old name for the flavor of classes now used for all class objects. In
earlier Python versions, only new-style classes could use Python’s newer,
versatile features like __slots__, descriptors,
properties, __getattribute__(), class methods, and static methods.

object

Any data with state (attributes or value) and defined behavior
(methods). Also the ultimate base class of any new-style
class.

package

A Python module which can contain submodules or recursively,
subpackages. Technically, a package is a Python module with an
__path__ attribute.

A named entity in a function (or method) definition that
specifies an argument (or in some cases, arguments) that the
function can accept. There are five kinds of parameter:

positional-or-keyword: specifies an argument that can be passed
either positionally or as a keyword argument. This is the default kind of parameter, for example foo
and bar in the following:

deffunc(foo,bar=None):...

positional-only: specifies an argument that can be supplied only
by position. Python has no syntax for defining positional-only
parameters. However, some built-in functions have positional-only
parameters (e.g. abs()).

keyword-only: specifies an argument that can be supplied only
by keyword. Keyword-only parameters can be defined by including a
single var-positional parameter or bare * in the parameter list
of the function definition before them, for example kw_only1 and
kw_only2 in the following:

deffunc(arg,*,kw_only1,kw_only2):...

var-positional: specifies that an arbitrary sequence of
positional arguments can be provided (in addition to any positional
arguments already accepted by other parameters). Such a parameter can
be defined by prepending the parameter name with *, for example
args in the following:

deffunc(*args,**kwargs):...

var-keyword: specifies that arbitrarily many keyword arguments
can be provided (in addition to any keyword arguments already accepted
by other parameters). Such a parameter can be defined by prepending
the parameter name with **, for example kwargs in the example
above.

Parameters can specify both optional and required arguments, as well as
default values for some optional arguments.

A provisional API is one which has been deliberately excluded from
the standard library’s backwards compatibility guarantees. While major
changes to such interfaces are not expected, as long as they are marked
provisional, backwards incompatible changes (up to and including removal
of the interface) may occur if deemed necessary by core developers. Such
changes will not be made gratuitously – they will occur only if serious
fundamental flaws are uncovered that were missed prior to the inclusion
of the API.

Even for provisional APIs, backwards incompatible changes are seen as
a “solution of last resort” - every attempt will still be made to find
a backwards compatible resolution to any identified problems.

This process allows the standard library to continue to evolve over
time, without locking in problematic design errors for extended periods
of time. See PEP 411 for more details.

Nickname for the Python 3.x release line (coined long ago when the
release of version 3 was something in the distant future.) This is also
abbreviated “Py3k”.

Pythonic

An idea or piece of code which closely follows the most common idioms
of the Python language, rather than implementing code using concepts
common to other languages. For example, a common idiom in Python is
to loop over all elements of an iterable using a for
statement. Many other languages don’t have this type of construct, so
people unfamiliar with Python sometimes use a numerical counter instead:

foriinrange(len(food)):print(food[i])

As opposed to the cleaner, Pythonic method:

forpieceinfood:print(piece)

qualified name

A dotted name showing the “path” from a module’s global scope to a
class, function or method defined in that module, as defined in
PEP 3155. For top-level functions and classes, the qualified name
is the same as the object’s name:

The number of references to an object. When the reference count of an
object drops to zero, it is deallocated. Reference counting is
generally not visible to Python code, but it is a key element of the
CPython implementation. The sys module defines a
getrefcount() function that programmers can call to return the
reference count for a particular object.

regular package

A traditional package, such as a directory containing an
__init__.py file.

A declaration inside a class that saves memory by pre-declaring space for
instance attributes and eliminating instance dictionaries. Though
popular, the technique is somewhat tricky to get right and is best
reserved for rare cases where there are large numbers of instances in a
memory-critical application.

sequence

An iterable which supports efficient element access using integer
indices via the __getitem__() special method and defines a
__len__() method that returns the length of the sequence.
Some built-in sequence types are list, str,
tuple, and bytes. Note that dict also
supports __getitem__() and __len__(), but is considered a
mapping rather than a sequence because the lookups use arbitrary
immutable keys rather than integers.

A form of generic function dispatch where the implementation is
chosen based on the type of a single argument.

slice

An object usually containing a portion of a sequence. A slice is
created using the subscript notation, [] with colons between numbers
when several are given, such as in variable_name[1:3:5]. The bracket
(subscript) notation uses slice objects internally.

special method

A method that is called implicitly by Python to execute a certain
operation on a type, such as addition. Such methods have names starting
and ending with double underscores. Special methods are documented in
Special method names.

statement

A statement is part of a suite (a “block” of code). A statement is either
an expression or one of several constructs with a keyword, such
as if, while or for.

struct sequence

A tuple with named elements. Struct sequences expose an interface similar
to named tuple in that elements can either be accessed either by
index or as an attribute. However, they do not have any of the named tuple
methods like _make() or
_asdict(). Examples of struct sequences
include sys.float_info and the return value of os.stat().

text encoding

A codec which encodes Unicode strings to bytes.

text file

A file object able to read and write str objects.
Often, a text file actually accesses a byte-oriented datastream
and handles the text encoding automatically.

A string which is bound by three instances of either a quotation mark
(”) or an apostrophe (‘). While they don’t provide any functionality
not available with single-quoted strings, they are useful for a number
of reasons. They allow you to include unescaped single and double
quotes within a string and they can span multiple lines without the
use of the continuation character, making them especially useful when
writing docstrings.

type

The type of a Python object determines what kind of object it is; every
object has a type. An object’s type is accessible as its
__class__ attribute or can be retrieved with
type(obj).

universal newlines

A manner of interpreting text streams in which all of the following are
recognized as ending a line: the Unix end-of-line convention '\n',
the Windows convention '\r\n', and the old Macintosh convention
'\r'. See PEP 278 and PEP 3116, as well as
bytes.splitlines() for an additional use.

A cooperatively isolated runtime environment that allows Python users
and applications to install and upgrade Python distribution packages
without interfering with the behaviour of other Python applications
running on the same system.